In collaborative filtering, interests of a user are predicted by collecting preferences or interest information from many similar users. For example, user A is interested in items A and C and user B is interested in item A, B, C, and D. If it is determined that user A and B are similar (e.g., through their respective profiles or through their common interests in items A and C), then it can be presumed that user A might be interested in other items that user B likes, such as items B and D in this example. Thus, items B and D may be recommended to user A according to the collaborative filtering model.
In this collaborative filtering model, only one kind of “interest” relationship exists, i.e., in only one direction. However, in other domains, such as where items represent people, the relationship is bi-directional. Focusing on a one-way relation in generating recommendations is unlikely to yield positive results.
The approaches described in this section are approaches that could be pursued, but not necessarily approaches that have been previously conceived or pursued. Therefore, unless otherwise indicated, it should not be assumed that any of the approaches described in this section qualify as prior art merely by virtue of their inclusion in this section.